Many localization problems leveraged supervised learning, requiring a labeled dataset associating each fingerprint vector to a labeled grid point, to train a classifier to classify the location.
The \(k\)  nearest neighbor (kNN) is the commonly used classification method for many fingerprint-based localizations, including the fingerprint-based on WiFi [1]} and beacon [2]}.
Besides kNN, support vector machine (SVM) [3]}, linear discriminant analysis (LDA) [4]}, and neural networks (NN) [5]} have also been widely exploited to improve localization performance.
All these classifiers are trained via a deterministic approach by explicitly mapping the input fingerprint vector to the associated output label.
Such approaches suffer severe performance degradation when RSS values in the fingerprint vector change due to unpredictable environmental factors.
